We present 4RC, a unified feed-forward framework for 4D reconstruction from monocular videos. Unlike existing approaches that typically decouple motion from geometry or produce limited 4D attributes such as sparse trajectories or two-view scene flow, 4RC learns a holistic 4D representation that jointly captures dense scene geometry and motion dynamics. At its core, 4RC introduces a novel encode-once, query-anywhere and anytime paradigm: a transformer backbone encodes the entire video into a compact spatio-temporal latent space, from which a conditional decoder can efficiently query 3D geometry and motion for any query frame at any target timestamp. To facilitate learning, we represent per-view 4D attributes in a minimally factorized form by decomposing them into base geometry and time-dependent relative motion. Extensive experiments demonstrate that 4RC outperforms prior and concurrent methods across a wide range of 4D reconstruction tasks.
4RC: 4D Reconstruction via Conditional Querying Anytime and Anywhere
4RC presents a unified feed-forward framework for 4D reconstruction from monocular videos that learns holistic scene geometry and motion dynamics through a transformer-based encoder-decoder architecture with conditional querying capabilities.
- Year
- 2026
- Venue
- arXiv 2026
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- 5
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- arxiv.org/abs/2602.10094ARXIV-DEFAULT
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